Abstract
Symptoms of nutrient deficiencies in rice plants often appear on the leaves. The leaf color and shape, therefore, can be used to diagnose nutrient deficiencies in rice. Image classification is an efficient and fast approach for this diagnosis task. Deep convolutional neural networks (DCNNs) have been proven to be effective in image classification, but their use to identify nutrient deficiencies in rice has received little attention. In the present study, we explore the accuracy of different DCNNs for diagnosis of nutrient deficiencies in rice. A total of 1818 photographs of plant leaves were obtained via hydroponic experiments to cover full nutrition and 10 classes of nutrient deficiencies. The photographs were divided into training, validation, and test sets in a 3 : 1 : 1 ratio. Fine-tuning was performed to evaluate four state-of-the-art DCNNs: Inception-v3, ResNet with 50 layers, NasNet-Large, and DenseNet with 121 layers. All the DCNNs obtained validation and test accuracies of over 90%, with DenseNet121 performing best (validation accuracy = 98.62 ± 0.57%; test accuracy = 97.44 ± 0.57%). The performance of the DCNNs was validated by comparison to color feature with support vector machine and histogram of oriented gradient with support vector machine. This study demonstrates that DCNNs provide an effective approach to diagnose nutrient deficiencies in rice.
Highlights
Fertilizers are essential to global food production, by ensuring high and stable yields of rice [1]. e best results come when specific fertilizers are applied in the needed amounts at the proper time
Deep convolutional neural networks (DCNNs) Experiments. e experiments were performed on a Windows10 desktop equipped with one Intel Core i9 7920X CPU with 64 GB RAM, accelerated by two GeForce GTX 1080Ti GPUs with 11 GB memory. e model implementation was powered by the Keras framework with the TensorFlow backend
Four DCNNs, Inception-v3, ResNet50, NasNet-Large, and DenseNet121, were used to diagnose various nutrient deficiencies in rice plants based on image recognition using a dataset collected from hydroponic experiments
Summary
Fertilizers are essential to global food production, by ensuring high and stable yields of rice [1]. e best results come when specific fertilizers are applied in the needed amounts at the proper time. Fertilizers are essential to global food production, by ensuring high and stable yields of rice [1]. E best results come when specific fertilizers are applied in the needed amounts at the proper time. Unscientific fertilization practices are common and, when coupled with a general delay between research findings and widespread adoption of technology, result in imbalanced nutrient application to rice fields. Ever greater amounts of fertilizers are applied to achieve only limited increases in rice yield, and the quality of the resulting rice declines [2]. Diagnosis of nutrient deficiencies in rice is an integral part of scientific fertilization, because soils often fail to completely meet the nutrient demands of growing plants. Determination of the needed nutrients will facilitate the formulation of a fertilization regime to supply the target nutrients without oversupplying others. Problems arise as agricultural production sites are extensive, and many nutrient deficiencies are widely
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